Revisiting common bug prediction findings using effort-aware models
Revisiting common bug prediction findings using effort-aware models
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DOI：
10.1109/icsm.2010.5609530
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发表时间：
2010-09
期刊：
影响因子：
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通讯作者：
Yasutaka Kamei;S. Matsumoto;Akito Monden;Ken-ichi Matsumoto;Bram Adams;A. Hassan
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文献类型：
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作者：
Yasutaka Kamei;S. Matsumoto;Akito Monden;Ken-ichi Matsumoto;Bram Adams;A. Hassan
错误预测模型通常用于帮助分配软件质量保证工作（例如测试和代码审查）。 Mende 和 Koschke 最近提出了具有努力意识的错误预测模型。这些模型在评估预测模型的有效性时考虑了审查或测试代码所需的工作量，从而实现更现实的性能评估。在本文中，我们重新审视错误预测文献中的两个常见发现：1）流程指标（例如更改历史记录）优于产品指标（例如 LOC），2）包级预测优于文件级预测。通过对 Eclipse 基金会的三个项目的案例研究，我们发现，当考虑努力时，第一个发现成立，而第二个发现则不成立。这些发现验证了错误预测文献中先前发现的实际意义，并鼓励它们在实践中采用。
Bug prediction models are often used to help allocate software quality assurance efforts (e.g. testing and code reviews). Mende and Koschke have recently proposed bug prediction models that are effort-aware. These models factor in the effort needed to review or test code when evaluating the effectiveness of prediction models, leading to more realistic performance evaluations. In this paper, we revisit two common findings in the bug prediction literature: 1) Process metrics (e.g., change history) outperform product metrics (e.g., LOC), 2) Package-level predictions outperform file-level predictions. Through a case study on three projects from the Eclipse Foundation, we find that the first finding holds when effort is considered, while the second finding does not hold. These findings validate the practical significance of prior findings in the bug prediction literature and encourage their adoption in practice.